Semiconductor manufacturing is one of the most complex processes in the manufacturing domain. In order to maintain the quality of finished wafers and good yield, accurate control of the processing equipment is essential to avoid drifts which may alter the process variables. Such drifts may be caused by drifts in the calibration of the mass flow controllers (MFC) which control the introduction of processing gases into the processing chamber, drifts in the calibration of the RF generator, leaks in the process chamber and turbopump malfunctions which result in the lack of pressure control.
Currently, process drifts are typically detected by a post-production examination of processed wafers. Such an after the fact approach is costly, inefficient, and results in the loss of processed wafers. As a result, several on-line approaches to controlling the process variables without drift have been implemented. Each of these currently available approaches however has been subject to substantial disadvantages. For example, recent techniques applied to the problem of controlling semiconductor processing have included first principle based modeling and statistical design of experiments. The first principle models are used for feedback control of selected process variables, for example, uniform temperature control across the wafer in rapid thermal processing. Such automatic control schemes are nonrobust in tuning themselves in response to process drifts introduced by other variables. This problem, however, is addressed by statistical methods wherein the process is modeled as a multivariate regression model. A well-designed experimental test plan and the response surface methodology are combined to identify the optimal set points for all the process variables. The relationship among the variables and the throughput of the process near the optimal region are captured in a multivariate regression model. During production, the actual output of the process is compared with the output of the regression model. Any resulting error, if beyond certain limits, is reduced by suitably adjusting the associated process variables through either feedforward or feedback control. One of the major drawbacks of this technique is the need for enormous amounts of experimental data. The number of experiments increases exponentially as a function of the number of variables as well as the number of variable states (levels). This is irrespective of how efficient the design of the experiment is. Further, for each experiment, a processed wafer must be measured for correlation of the input variables to the process output. In summary, because the data collection is laborious and costly the response surface methodology is not an attractive option, especially for use in factory applications.
A better methodology for process optimization would be process diagnostics, i.e. to diagnose any drift in any process variable and correct its source rather than compensate for it. One of the primary reasons for any drift is a change in equipment state. Artificial (AI) based techniques have been applied for the diagnostics of changes in the equipment state. For example, rule based expert systems have been developed for diagnosing plasma etcher performance using the end point trace. This technique however is an off-line methodology (i.e. an after the fact approach) and further, does not precisely point to the source of the problem but simply indicates that some abnormality exists. Essentially, this approach is inadequate for a process control. The Dempster-Schafer approach to equipment diagnosis has been employed in low pressure chemical vapor deposition and plasma etch reactors. This diagnostic system utilizes maintenance history, real time sensory data, and post-process measurements to diagnose various equipment problems. One of the major limitations of the Dempster-Schafer technique is the conditional independence assumptions in the evidence space as well as the fault space. These assumptions result in an over-assertive diagnosis, i.e. the belief in the result is exaggerated. Further, while this technique has been demonstrated for variables that can be measured deterministically, such as temperature and pressure drifts, it has never been proven for in-situ diagnosis of drifts in uncertain variables like gas flows that are difficult to measure.